Algorithms¶
aif360.algorithms.preprocessing¶
algorithms.preprocessing.DisparateImpactRemover([…]) |
Disparate impact remover is a preprocessing technique that edits feature values increase group fairness while preserving rank-ordering within groups [1]_. |
algorithms.preprocessing.LFR(…[, k, Ax, …]) |
Learning fair representations is a pre-processing technique that finds a latent representation which encodes the data well but obfuscates information about protected attributes [2]_. |
algorithms.preprocessing.OptimPreproc(…[, …]) |
Optimized preprocessing is a preprocessing technique that learns a probabilistic transformation that edits the features and labels in the data with group fairness, individual distortion, and data fidelity constraints and objectives [3]_. |
algorithms.preprocessing.Reweighing(…) |
Reweighing is a preprocessing technique that Weights the examples in each (group, label) combination differently to ensure fairness before classification [4]_. |
aif360.algorithms.inprocessing¶
algorithms.inprocessing.AdversarialDebiasing(…) |
Adversarial debiasing is an in-processing technique that learns a classifier to maximize prediction accuracy and simultaneously reduce an adversary’s ability to determine the protected attribute from the predictions [5]_. |
algorithms.inprocessing.ARTClassifier(…) |
Wraps an instance of an art.classifiers.Classifier to extend Transformer. |
algorithms.inprocessing.GerryFairClassifier([…]) |
Model is an algorithm for learning classifiers that are fair with respect to rich subgroups. |
algorithms.inprocessing.MetaFairClassifier([…]) |
The meta algorithm here takes the fairness metric as part of the input and returns a classifier optimized w.r.t. |
algorithms.inprocessing.PrejudiceRemover([…]) |
Prejudice remover is an in-processing technique that adds a discrimination-aware regularization term to the learning objective [6]_. |
aif360.algorithms.postprocessing¶
algorithms.postprocessing.CalibratedEqOddsPostprocessing(…) |
Calibrated equalized odds postprocessing is a post-processing technique that optimizes over calibrated classifier score outputs to find probabilities with which to change output labels with an equalized odds objective [7]_. |
algorithms.postprocessing.EqOddsPostprocessing(…) |
Equalized odds postprocessing is a post-processing technique that solves a linear program to find probabilities with which to change output labels to optimize equalized odds [8]_ [9]_. |
algorithms.postprocessing.RejectOptionClassification(…) |
Reject option classification is a postprocessing technique that gives favorable outcomes to unpriviliged groups and unfavorable outcomes to priviliged groups in a confidence band around the decision boundary with the highest uncertainty [10]_. |
aif360.algorithms¶
algorithms.Transformer(**kwargs) |
Abstract base class for transformers. |